六十七、Spark-两种运行方式（本地运行，提交集群运行）
【摘要】
本地运行：在IDEA中直接运行控制台输出结果即可
集群运行：在本地将程序打包为 jar，提交至集群运行其程序（将结果上传至hdfs）
文章目录
一、本地运行spark程序
二、集群运行spark程序
一、本地运行spark程序
 ...
本地运行：在IDEA中直接运行控制台输出结果即可
集群运行：在本地将程序打包为 jar，提交至集群运行其程序（将结果上传至hdfs）
文章目录
一、本地运行spark程序
1、pom依赖
注：依赖配置项及其版本一定要与集群环境相适配
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<?xml version="1.0" encoding="UTF-8"?>
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<project xmlns="http://maven.apache.org/POM/4.0.0"
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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<groupId>cn.itcast</groupId>
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<artifactId>SparkDemo</artifactId>
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<version>1.0-SNAPSHOT</version>
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<repositories>
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<repository>
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<id>aliyun</id>
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<url>http://maven.aliyun.com/nexus/content/groups/public/</url>
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</repository>
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<repository>
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<id>apache</id>
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<url>https://repository.apache.org/content/repositories/snapshots/</url>
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</repository>
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<repository>
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<id>cloudera</id>
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<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
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</repository>
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</repositories>
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<properties>
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<encoding>UTF-8</encoding>
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<maven.compiler.source>1.8</maven.compiler.source>
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<maven.compiler.target>1.8</maven.compiler.target>
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<scala.version>2.12.11</scala.version>
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<spark.version>3.0.1</spark.version>
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<hadoop.version>2.7.5</hadoop.version>
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</properties>
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<dependencies>
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<!--依赖Scala语言-->
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<dependency>
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<groupId>org.scala-lang</groupId>
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<artifactId>scala-library</artifactId>
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<version>${scala.version}</version>
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</dependency>
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<!--SparkCore依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-core_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!-- spark-streaming-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-streaming_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!--spark-streaming+Kafka依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!--SparkSQL依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-sql_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!--SparkSQL+ Hive依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-hive_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-hive-thriftserver_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!--StructuredStreaming+Kafka依赖-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-sql-kafka-0-10_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<!-- SparkMlLib机器学习模块,里面有ALS推荐算法-->
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<dependency>
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<groupId>org.apache.spark</groupId>
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<artifactId>spark-mllib_2.12</artifactId>
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<version>${spark.version}</version>
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</dependency>
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<dependency>
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<groupId>org.apache.hadoop</groupId>
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<artifactId>hadoop-client</artifactId>
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<version>2.7.5</version>
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</dependency>
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<dependency>
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<groupId>com.hankcs</groupId>
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<artifactId>hanlp</artifactId>
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<version>portable-1.7.7</version>
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</dependency>
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<dependency>
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<groupId>mysql</groupId>
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<artifactId>mysql-connector-java</artifactId>
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<version>8.0.23</version>
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</dependency>
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<dependency>
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<groupId>redis.clients</groupId>
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<artifactId>jedis</artifactId>
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<version>2.9.0</version>
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</dependency>
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<dependency>
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<groupId>com.alibaba</groupId>
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<artifactId>fastjson</artifactId>
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<version>1.2.47</version>
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</dependency>
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<dependency>
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<groupId>org.projectlombok</groupId>
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<artifactId>lombok</artifactId>
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<version>1.18.2</version>
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<scope>provided</scope>
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</dependency>
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</dependencies>
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<build>
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<sourceDirectory>src/main/scala</sourceDirectory>
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<plugins>
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<!-- 指定编译java的插件 -->
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-compiler-plugin</artifactId>
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<version>3.5.1</version>
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</plugin>
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<!-- 指定编译scala的插件 -->
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<plugin>
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<groupId>net.alchim31.maven</groupId>
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<artifactId>scala-maven-plugin</artifactId>
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<version>3.2.2</version>
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<executions>
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<execution>
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<goals>
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<goal>compile</goal>
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<goal>testCompile</goal>
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</goals>
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<configuration>
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<args>
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<arg>-dependencyfile</arg>
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<arg>${project.build.directory}/.scala_dependencies</arg>
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</args>
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</configuration>
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</execution>
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</executions>
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</plugin>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-surefire-plugin</artifactId>
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<version>2.18.1</version>
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<configuration>
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<useFile>false</useFile>
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<disableXmlReport>true</disableXmlReport>
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<includes>
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<include>**/*Test.*</include>
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<include>**/*Suite.*</include>
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</includes>
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</configuration>
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</plugin>
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-shade-plugin</artifactId>
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<version>2.3</version>
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<executions>
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<execution>
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<phase>package</phase>
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<goals>
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<goal>shade</goal>
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</goals>
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<configuration>
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<filters>
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<filter>
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<artifact>*:*</artifact>
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<excludes>
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<exclude>META-INF/*.SF</exclude>
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<exclude>META-INF/*.DSA</exclude>
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<exclude>META-INF/*.RSA</exclude>
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</excludes>
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</filter>
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</filters>
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<transformers>
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<transformer
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implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
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<mainClass></mainClass>
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</transformer>
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</transformers>
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</configuration>
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</execution>
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</executions>
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</plugin>
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</plugins>
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</build>
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</project>
2、数据展示
3、代码编写
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package org.example.spark
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import org.apache.spark.rdd.RDD
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import org.apache.spark.{SparkConf, SparkContext}
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object word {
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def main(args: Array[String]): Unit = {
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//准备环境
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val conf = new SparkConf().setMaster("local[*]").setAppName("wordcount")
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val sc = new SparkContext(conf)
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//加载文件
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val rdd1: RDD[String] = sc.textFile("data/input/words.txt")
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// 处理数据
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val rdd2: RDD[String] = rdd1.flatMap(lp => {
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lp.split(" ")
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})
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val rdd3: RDD[(String, Int)] = rdd2.map(it => (it, 1))
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val rdd4: RDD[(String, Int)] = rdd3.reduceByKey((curr, agg) => curr + agg)
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val result: Array[(String, Int)] = rdd4.collect()
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result.foreach(i => println(i))
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}
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}
4、本地运行
注：单词统计案例本地效果如图所示
二、集群运行spark程序
1、修改代码
val rdd1: RDD[String] = sc.textFile("hdfs:///input/wordcount.txt")
rdd4.saveAsTextFile("hdfs://192.168.231.247:8020/output/output1")
注：集群运行文件加载路径设置为hdfs，即每次集群运行从hdfs拿取数据，并将实时数据上传至hdfs
2、打包jar
注：双击maven中的package，maven会自动进行清除缓存，测试并打包为jar
3、找到项目路径中的jar包
注：jar包大小最小的为不是带全部依赖的jar包，在集群运行不需要全部的依赖，即上传最小依赖的jar包即可
4、上传至linux
注：此处使用xftp进行传输 jar包
5、启动 hadoop 以及 spark 集群
6、进入spark安装目录下执行
bin/spark-submit --class org.example.spark.word --master spark://master:8020 /input/original-SparkDemo-1.0-SNAPSHOT.jar
注：单词统计集群运行如图所示
7、进入hdfs web端目录进行查看
Spark-两种运行方式（本地运行，提交集群运行）完成
文章来源: tuomasi.blog.csdn.net，作者：托马斯-酷涛，版权归原作者所有，如需转载，请联系作者。
原文链接：tuomasi.blog.csdn.net/article/details/122919106
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